Statistical landslide susceptibility mapping (LSM) models have been most widely used in
literatures. However, limitations and uncertainties remain in these methods. The main goal …

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention
and mitigation. However, delineating the spatial occurrence pattern of the landslide remains …

Landslide susceptibility assessment of Uttarakhand area of India has been done by applying
five machine learning methods namely Support Vector Machines (SVM), Logistic …

In this study, a novel coupling model for landslide susceptibility mapping is presented. In
practice, environmental factors may have different impacts at a local scale in study areas. To …

Landslides can cause severe damage to both the environment and society, and many
statistical, index-based, and inventory-based methods have been developed to assess …

The risks associated with landslides are increasing the personal losses and material
damages in more and more areas of the world. These natural disasters are related to …

The main objective of the present study is to introduce a novel predictive model that
combines evolutionary algorithms and machine learning (ML) models, so as to construct a …

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to
occur more frequently due to increasing urbanization, deforestation, and climate change …

Data driven methods are widely used for the development of Landslide Susceptibility
Mapping (LSM). The results of these methods are sensitive to different factors, such as the …

The main purpose of this study was to produce landslide susceptibility maps using various
ensemble-based machine learning models (ie, the AdaBoost, LogitBoost, Multiclass …